Full GHC Haskell on Truffle (GraalVM).
A diff viewer for German amendment acts.
Give it the amendment act (Änderungsgesetz or Änderungsantrag) and its principal (Stammnorm). It outputs a two-column, colored before/after synopsis.
Made with Claude’s help, by yours truly.
The code (GitHub) is written in Java. The web version is a GraalVM web image.
Turns Java bytecode into WebAssembly.
A Rust compiler back end for Java bytecode. Comes with a nice-looking Java FFI.
A subset of BEAM for small devices. Enables you to program microcontrollers with Erlang, Elixir, or Gleam.
Compares occupational pension (bAV), the upcoming Altersvorsorgedepot, and a private ETF depending on your situation.
Made with Claude, by yours truly. Available in German, English, Latin, and Japanese among others.
It isn’t legal or financial advice, I haven’t verified its accuracy meticulously, and it probably doesn’t catch all cases and possible inputs. But it’s very likely better than I could do by hand.
Keep in mind, too, that there are many additional factors other than the pure numbers game that you have to take into consideration and that ultimately you can only evaluate yourself, such as:
- liquidity / flexibility
- availability of funds for early retirement
- psychology / savings discipline
- trust in the financial system / insurance companies / the government
Not zero! But also far from 100%.
I suppose one thing that’s convenient about short-form blogging (like what I do here) is how the question doesn’t even really arise because doing all your writing yourself is only just as much work as coming up with a prompt for an AI to write the same thing.
Another sandbox for AI agents.
Shows you your coding agent usage stats.
You don’t say.
A Python–C++ binding helper library.
Advanced general-purpose physics simulation library with both C and C++ APIs. Also has official Python bindings.
Another sandbox for AI agents. By NVIDIA.
“Breakthrough Method for Agile AI-Driven Development.” I have no idea what it is, but it sounds cool.
A game engine for Common Lisp.
Ruby for microcontrollers.
In AI, whatever scales with compute wins. Human preconceptions are one example of what always loses out in the long run.
This is because our human way of thinking rests on simplifications and abstractions, both of which progressively lose value the more compute you have.
The Bitter Lesson: In AI, whatever scales with compute wins. Human preconceptions are one example of what does not scale well.
Many AI agent harnesses get this wrong and encode their human creators’ ideas about workflows too strongly.
Reddit for AI agents (as opposed to for humans).
A Gerbil Scheme REPL MCP server for coding agents.
REPL-driven workflows have been agent-unfriendly so far, so I welcome this. Personally I would have implemented a Claude Code skill instead of an MCP server, but as long as it works…